Multicellular tissues are shaped and remodeled during development, organogenesis, wound healing, cancer, and regeneration, as well as during a broad range of diseases. Time-lapse sequences of tissue movements convey a wealth of information about the processes driving their self-assembly and mechanobiology. Here we present StrainMapperJ, a digital image correlation (DIC) tool and supporting macros for the widely used open-source image analysis package ImageJ. StrainMapperJ can produce a diverse set of graphics for mechanical data exploration. At its core, StrainMapperJ uses the ImageJ plugin bUnwarpJ to map deformations in one image to match a second. From these results, StrainMapperJ can generate 2D maps of mechanical strain in the image frame, principal engineering strain and orientation, area strains, synthetic movement trajectories, and vorticity. StrainMapperJ can generate a variety of graphical outputs for quantitative analysis that registers or maps mechanical information directly onto the source image sequence. Additionally, the algorithms used in StrainMapperJ can yield quantitative information from short time-lapse sequences, often only a few minutes in duration, reducing the need for long-term imaging. In addition to these macros, we provide guidance on preparing image datasets, troubleshooting the analysis pipeline, and interpreting StrainMapperJ-produced maps for hypothesis generation and testing.